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#' Print model summary
#'
#' Display basic information about a \code{gbm} object.
#'
#' Prints some information about the model object. In particular, this method
#' prints the call to \code{gbm()}, the type of loss function that was used,
#' and the total number of iterations.
#'
#' If cross-validation was performed, the 'best' number of trees as estimated
#' by cross-validation error is displayed. If a test set was used, the 'best'
#' number of trees as estimated by the test set error is displayed.
#'
#' The number of available predictors, and the number of those having non-zero
#' influence on predictions is given (which might be interesting in data mining
#' applications).
#'
#' If multinomial, bernoulli or adaboost was used, the confusion matrix and
#' prediction accuracy are printed (objects being allocated to the class with
#' highest probability for multinomial and bernoulli). These classifications
#' are performed on the entire training data using the model with the 'best'
#' number of trees as described above, or the maximum number of trees if the
#' 'best' cannot be computed.
#'
#' If the 'distribution' was specified as gaussian, laplace, quantile or
#' t-distribution, a summary of the residuals is displayed. The residuals are
#' for the training data with the model at the 'best' number of trees, as
#' described above, or the maximum number of trees if the 'best' cannot be
#' computed.
#'
#' @aliases print.gbm show.gbm
#' @param x an object of class \code{gbm}.
#' @param \dots arguments passed to \code{print.default}.
#' @author Harry Southworth, Daniel Edwards
#' @seealso \code{\link{gbm}}
#' @keywords models nonlinear survival nonparametric
#' @examples
#'
#' data(iris)
#' iris.mod <- gbm(Species ~ ., distribution="multinomial", data=iris,
#' n.trees=2000, shrinkage=0.01, cv.folds=5,
#' verbose=FALSE, n.cores=1)
#' iris.mod
#' #data(lung)
#' #lung.mod <- gbm(Surv(time, status) ~ ., distribution="coxph", data=lung,
#' # n.trees=2000, shrinkage=0.01, cv.folds=5,verbose =FALSE)
#' #lung.mod
#' @rdname print.gbm
#' @export
print.gbm <- function(x, ... )
{
if (!is.null(x$call)){ print(x$call) }
dist.name <- x$distribution$name
if (dist.name == "pairwise")
{
if (!is.null(x$distribution$max.rank) && x$distribution$max.rank > 0)
{
dist.name <- sprintf("pairwise (metric=%s, max.rank=%d)", x$distribution$metric, x$distribution$max.rank)
}
else
{
dist.name <- sprintf("pairwise (metric=%s)", x$distribution$metric)
}
}
cat( paste( "A gradient boosted model with", dist.name, "loss function.\n" ))
cat( paste( length( x$train.error ), "iterations were performed.\n" ) )
best <- length( x$train.error )
if ( !is.null( x$cv.error ) )
{
best <- gbm.perf( x, plot.it = FALSE, method="cv" )
cat( paste("The best cross-validation iteration was ", best, ".\n", sep = "" ) )
}
if ( x$train.fraction < 1 )
{
best <- gbm.perf( x, plot.it = FALSE, method="test" )
cat( paste("The best test-set iteration was ", best, ".\n", sep = "" ) )
}
if ( is.null( best ) )
{
best <- length( x$train.error )
}
ri <- relative.influence( x, n.trees=best )
cat( "There were", length( x$var.names ), "predictors of which",
sum( ri > 0 ), "had non-zero influence.\n" )
invisible()
}
#' @rdname print.gbm
#'
#' @export
show.gbm <- print.gbm
#' Summary of a gbm object
#'
#' Computes the relative influence of each variable in the gbm object.
#'
#' For \code{distribution="gaussian"} this returns exactly the reduction of
#' squared error attributable to each variable. For other loss functions this
#' returns the reduction attributable to each variable in sum of squared error
#' in predicting the gradient on each iteration. It describes the relative
#' influence of each variable in reducing the loss function. See the references
#' below for exact details on the computation.
#'
#' @param object a \code{gbm} object created from an initial call to
#' \code{\link{gbm}}.
#' @param cBars the number of bars to plot. If \code{order=TRUE} the only the
#' variables with the \code{cBars} largest relative influence will appear in
#' the barplot. If \code{order=FALSE} then the first \code{cBars} variables
#' will appear in the plot. In either case, the function will return the
#' relative influence of all of the variables.
#' @param n.trees the number of trees used to generate the plot. Only the first
#' \code{n.trees} trees will be used.
#' @param plotit an indicator as to whether the plot is generated.
#' @param order an indicator as to whether the plotted and/or returned relative
#' influences are sorted.
#' @param method The function used to compute the relative influence.
#' \code{\link{relative.influence}} is the default and is the same as that
#' described in Friedman (2001). The other current (and experimental) choice is
#' \code{\link{permutation.test.gbm}}. This method randomly permutes each
#' predictor variable at a time and computes the associated reduction in
#' predictive performance. This is similar to the variable importance measures
#' Breiman uses for random forests, but \code{gbm} currently computes using the
#' entire training dataset (not the out-of-bag observations).
#' @param normalize if \code{FALSE} then \code{summary.gbm} returns the
#' unnormalized influence.
#' @param ... other arguments passed to the plot function.
#' @return Returns a data frame where the first component is the variable name
#' and the second is the computed relative influence, normalized to sum to 100.
#' @author Greg Ridgeway \email{gregridgeway@@gmail.com}
#' @seealso \code{\link{gbm}}
#' @references J.H. Friedman (2001). "Greedy Function Approximation: A Gradient
#' Boosting Machine," Annals of Statistics 29(5):1189-1232.
#'
#' L. Breiman
#' (2001).\url{https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf}.
#' @keywords hplot
#'
#' @export summary.gbm
#' @export
summary.gbm <- function(object,
cBars=length(object$var.names),
n.trees=object$n.trees,
plotit=TRUE,
order=TRUE,
method=relative.influence,
normalize=TRUE,
...)
{
if(n.trees < 1)
{
stop("n.trees must be greater than 0.")
}
if(n.trees > object$n.trees)
{
warning("Exceeded total number of GBM terms. Results use n.trees=",object$n.trees," terms.\n")
n.trees <- object$n.trees
}
rel.inf <- method(object,n.trees)
rel.inf[rel.inf<0] <- 0
if(order)
{
i <- order(-rel.inf)
}
else
{
i <- 1:length(rel.inf)
}
if(cBars==0) cBars <- min(10,length(object$var.names))
if(cBars>length(object$var.names)) cBars <- length(object$var.names)
if(normalize) rel.inf <- 100*rel.inf/sum(rel.inf)
if(plotit)
{
barplot(rel.inf[i[cBars:1]],
horiz=TRUE,
col=rainbow(cBars,start=3/6,end=4/6),
names=object$var.names[i[cBars:1]],
xlab="Relative influence",...)
}
return(data.frame(var=object$var.names[i],
rel.inf=rel.inf[i]))
}
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